M-plicits: Neural Implicit Surfaces via Nested Multiscale Residuals

arXiv:2609.28684 · cs.CV, cs.GR, cs.LG · Submitted 2026-09-23 · Read on arXiv

cs.CV, cs.GR, cs.LG

Submitted: 2026-09-23

Updated: 2026-09-25

Code: https://github.com/dsilvavinicius/m-plicits

Project page: https://dsilvavinicius.github.io/differential_geometry_in_neural_implicits

Terminology

• Chamfer Distance — 23× in this paper
• Robustness — 18× in this paper · explained in 5 episodes
• Ablation — 13× in this paper · explained in 1 episode
• Fidelity — 10× in this paper · explained in 2 episodes
• Point Clouds — 9× in this paper · explained in 2 episodes
• Regularization — 7× in this paper · explained in 3 episodes
• Ablation Studies — 6× in this paper · explained in 1 episode
• Trace — 5× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Adam — 4× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 4× in this paper
• ACM Transactions on Graphics — 3× in this paper
• Alignment — 3× in this paper · explained in 1 episode
• Loss Function — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Implicit Displacement Fields — 2× in this paper
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Digital Twin — 1× in this paper · explained in 5 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Topological Defects — 1× in this paper · explained in 2 episodes

Sources

• On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes
• Implicit Geometric Regularization for Learning Shapes
• BACON: Band-limited Coordinate Networks for Multiscale Scene Representation
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Accelerating 3D Deep Learning with PyTorch3D
• NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
• PR-NeuS: A Prior-based Residual Learning Paradigm for Fast Multi-view Neural Surface Reconstruction
• Thingi10K: A Dataset of 10,000 3D-Printing Models

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